ISSN2320-2882
Impact Factor9.17
UGC ParametersAligned
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CrossRefDOI
ESTD2013
Peer ReviewTransparent
AccessOpen
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Paper Title
Customer Churn Prediction Marketing
Authors
R PARASURAMAN , H SUBASH , A MOHAMED RASHID FARHAN , MERVIN AJAY AMALAN , M SAKTHIVANITHA
Keywords
Primary Keywords: Customer Churn Prediction Machine Learning Random Forest Classifier SMOTE (Synthetic Minority Oversampling Technique) Hyperparameter Optimization GridSearchCV ROC AUC System & Architecture Keywords: Full-Stack Machine Learning System Real-Time Prediction System Flask Web Application RESTful API SQLAlchemy ORM Bootstrap Dashboard Advanced ML & Analytics: SHAP Explainability Feature Engineering Predictive Analytics Customer Segmentation Risk Classification Performance &am
Abstract
This paper presents ChurnShield, an enterprise-grade, full-stack machine learning platform designed for real-time customer churn prediction in subscription-based and marketing-driven business environments. ChurnShield integrates a Random Forest classifier, augmented by SMOTE-based class balancing and GridSearchCV hyperparameter optimisation, to achieve 95.25% accuracy and 98.86% ROC AUC on a real-world telecom customer dataset of 7,043 records. The system eliminates reactive churn management through a five-stage ML pipeline encompassing data ingestion, feature engineering, model training, SHAP-based explainability, and automated customer segmentation into four actionable risk tiers. A Flask-based RESTful backend with SQLAlchemy ORM and a Bootstrap 5 administrative dashboard deliver sub-second individual predictions and batch processing of 10,000 records in under 3 seconds. Security design implements defence-in-depth across authentication (bcrypt + JWT), application (CSRF protection), and data layers (parameterised SQL). Benchmarking results, system architecture, database design, and a future roadmap including deep learning and federated learning integration are presented.Index Terms--churn prediction, machine learning, random forest, SHAP explainability, customer retention, Flask, scikit-learn, class imbalance, SMOTE, web application.
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IJCRT — journal style
"Customer Churn Prediction Marketing", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 4, page no.m143-m147, April 2026, Available :https://ijcrt.org/papers/IJCRT26A4411.pdf
APA — 7th edition
PARASURAMAN, R., SUBASH, H., FARHAN, A. M. R., AMALAN, M. A., & SAKTHIVANITHA, M. (2026). Customer Churn Prediction Marketing. International Journal of Creative Research Thoughts, 14(4), m143-m147. https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4411
MLA — 9th edition
PARASURAMAN, R, et al. "Customer Churn Prediction Marketing." International Journal of Creative Research Thoughts, vol. 14, no. 4, 2026, pp. m143-m147, https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4411.
Chicago — 17th, bibliography
PARASURAMAN, R, et al. "Customer Churn Prediction Marketing." International Journal of Creative Research Thoughts 14, no. 4 (2026): m143-m147. https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4411.
Harvard — author–date
PARASURAMAN, R. et al. (2026) 'Customer Churn Prediction Marketing', International Journal of Creative Research Thoughts, 14(4), pp. m143-m147. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4411
IEEE — numbered reference
R. PARASURAMAN, H. SUBASH, A. M. R. FARHAN, M. A. AMALAN and M. SAKTHIVANITHA, "Customer Churn Prediction Marketing," IJCRT, vol. 14, no. 4, pp. m143-m147, Apr. 2026.
Vancouver — biomedical
PARASURAMAN R, SUBASH H, FARHAN AMR, AMALAN MA, SAKTHIVANITHA M. Customer Churn Prediction Marketing. IJCRT. 2026 Apr;14(4):m143-m147.
AMA — 11th edition
PARASURAMAN R, SUBASH H, FARHAN AMR, AMALAN MA, SAKTHIVANITHA M. Customer Churn Prediction Marketing. IJCRT. 2026;14(4):m143-m147. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4411
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT26A4411,
author = {R PARASURAMAN and H SUBASH and A MOHAMED RASHID FARHAN and MERVIN AJAY AMALAN and M SAKTHIVANITHA},
title = {Customer Churn Prediction Marketing},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {4},
pages = {m143-m147},
year = {2026},
month = {April},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4411}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - PARASURAMAN, R
AU - SUBASH, H
AU - FARHAN, A MOHAMED RASHID
AU - AMALAN, MERVIN AJAY
AU - SAKTHIVANITHA, M
TI - Customer Churn Prediction Marketing
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 4
PY - 2026
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4411
SP - m143-m147
ER -
Issue
Volume 14 Issue 4,
April-2026
Pages : m143-m147
Article Information
- Pages
- m143-m147
- Published
- April 2026
- Registration ID
- IJCRT_307494
- Published Paper ID
- IJCRT26A4411
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- CHENNAI, TAMILNADU, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 138
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4411
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT26A4411.pdf
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International Journal of Creative Research Thoughts
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2320-2882
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